我院助理教授王武毅的合作论文“Identifying Latent Group Structures in Nonlinear Panels”（合作者：Liangjun Su）被计量经济学领域顶级期刊Journal of Econometrics接受发表。
We propose a procedure to identify latent group structures in nonlinear panel data models where some regression coefficients are heterogeneous across groups but homogeneous within a group and the group number and membership are unknown. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we extend the sequential binary segmentation algorithm of Bai (1997) for break detection from the time series setup to the panel data framework. We demonstrate that our method is able to identify the true latent group structures with probability approaching one and the post-classification estimators are oracle-efficient. The method has the advantage of more convenient implementation compared with some alternative methods, which is a desirable feature in nonlinear panel applications. To improve the finite sample performance, we also consider an alternative version based on the spectral decomposition of certain estimated matrix and link the group identification issue to the community detection problem in the network literature. Simulations show that our method has good finite sample performance. We apply this method to explore how individuals' portfolio choices respond to their financial status and other characteristics using the Netherlands household panel data from year 1993 to 2015, and find three latent groups.
王武毅，现任暨南大学经济与社会研究院助理教授，2018年获得新加坡管理大学经济学博士学位。他的研究领域为理论计量经济学、应用计量经济学，研究成果发表于Journal of Econometrics、Journal of Applied Econometrics、Economics Letters、IEEE Transactions on Information Theory等国际知名期刊。